The importance of weather

A discussion which has been lacking in most of the news articles, and is very important to consider, is the influence of weather when investigating changes in air pollutant concentrations. It is obvious that emissions of pollutants would have reduced throughout Europe due the the COVID-19 lockdown measures from curtailed economic activities. This would have resulted in lower concentrations and better air quality in many locations. However, the same effect of decreasing concentrations would be observed if, for example, wind speed and atmospheric dispersion had increased. This is a classic issue and question in air quality interpretation: “Are the changes observed due to emissions or weather?” Here, we take into account changes in weather so we can more robustly determine what effect the lockdown had on air quality. A similar analysis has been conducted in the United Kingdom by David Carslaw, a previous supervisor and a colleague.^[8]

All data and analyses are preliminary at this stage and research is ongoing.

First checks

Time series of pollutant concentrations at selected monitoring sites in March 2020 are shown in Figure 2. It seems that concentrations of NO₂, NO_x, and O₃ did not drastically change after the lockdown was implemented. There are however examples, such as NO₂ and NO_x at Lugano-Università, where concentrations have visibly decreased after the lockdown, indicating that concentrations and lockdown measures are connected. Therefore, additional, more in-depth exploration is needed to help explain the patterns seen in Figure 2.

Figure 2: Time series of daily NO₂, NO_x, and O₃ in March 2020 for selected of monitoring sites in Switzerland.

When starting an analysis such as this, it is important to consider what the general state-of-play of weather conditions have been for the analysis period. The weather in the first three months of 2020 (winter and the first month of spring) has been mild in most of Switzerland.

In Figure 3, it can been seen that average air temperatures in the beginning of 2020 have been higher in almost all locations plotted compared to the last few years. In some locations, such as Basel and Zürich, wind speed has also been unusually high. The warmer temperatures and higher wind speeds have likely led to a less stable planetary boundary layer (the lowest portion of the atmosphere). Although this may not have been universal across all of Switzerland, the dispersion characteristics of the atmosphere have likely been enhanced in the January–March 2020 period compared to previous years in many locations. These observations suggest that emissions of pollutants may have been lower than the last previous years, and in some locations there would have been enhanced dispersion.

Figure 3: Means of meteorological variables for a number of locations across Switzerland between January, and March for 2017, 2018, 2019, and 2020.

Mean pollutant concentrations for the same periods (between January and March for 2017, 2018, 2019, and 2020) are displayed in Figure 4. Here, we can see for NO₂ and NO_x, that concentrations are generally lower in 2020 than in previous years. In some locations such as Bern-Bollwerk, Härkingen-A1, and Payerne the mean NO₂ and NO_x concentrations in 2020 are much lower than in previous years. However, for O₃, this is not the case.

Figure 4: Pollutant concentration means for selected monitoring sites in Switzerland between January, and March for 2017, 2018, 2019, and 2020.

The aggregations in 2020 shown in Figure 4 include two weeks of lockdown measures, so this question can be asked: "Are the lockdown measures responsible for the observed decreases?’’ Based on the meteorological data shown in Figure 3, it is expected that concentrations may have been lower due to a mild winter and increased atmospheric dispersion in many locations in Switzerland. Therefore, further work is required to disentangle the influence of the lockdown measures and the potentially confounding meteorological factors.

Modelling pollutant concentrations for March 2020

After the random forest models were trained, checked for adequate skill, and used to predict concentrations of NO₂, NO_x, and O₃ for March 2020, the observed concentrations were compared to those which were predicted. Figure 5 shows daily time series of observed and predicted concentrations for March 2020 for the sites included in the analysis. For some sites, such as Bern-Bollwerk and Lugano-Università, a clear divergence is seen between the NO₂ observed and predicted values after the March 16 lockdown date. The observed concentrations were lower than the predicted values suggesting that actual NO₂ concentrations were lower than those based on a business as usual scenario. The differences (delta) between the observed and predicted values are also shown in Figure 6.

Figure 5: Observed and predicted NO₂, NO_x, and O₃ concentrations for March 2020 for selected Switzerland’s monitoring sites.

Figure 6: Delta of observed and predicted NO₂, NO_x, and O₃ concentrations for March 2020 for the selected Switzerland’s monitoring sites

To more clearly expose the differences between the observed and predicted values, cumulative sums (cumsum) were calculated for the selected sites’ pollutants. This technique simply aggregates the deltas/differences over time and allows for identification of both the direction and time when the divergence occurred. Cumulative sums of NO₂, NO_x, and O₃ for the selected sites are displayed in Figure 7.

Figure 7: Cumulative sum for observed and predicted NO₂, NO_x, and O₃ values for March 2020 for selected Switzerland’s monitoring sites.

Figure 7 is a key figure in many ways because the changes shown are relative to the business as usual scenario. First, it shows that NO₂ and NO_x have decreased across most of Switzerland, but for the rural sites (Beromünster and Payerne), this decrease has been much less dramatic. For Bern-Bollwerk, Lausanne-César-Roux, and Härkingen-A1 (all traffic sites), the divergence from the business as usual scenario occurs immediately at the start of March which suggests that concentrations of NO₂ and NO_x were lower than what the model predicts two weeks before the lockdown was announced. This could be explained by NO_x emissions already being lower than normal two weeks before the lockdown, or limited skill of the model from using the surface meteorological variables for training—or perhaps a combination of both. These features in Figure 7 link back to the interpretation of the simple aggregations shown in Figure 3 and Figure 4. Because these three sites are classed as traffic sites (Table 1), the decreases can be interpreted as lower emissions of these pollutants in close proximity to the monitoring sites.

Figure 7 also suggests that O₃ concentrations have been higher in March 2020 than the business as usual scenario. O₃ is not a primary (directly emitted) pollutant and is generated in the atmosphere. O₃ and NO_x are linked by a transformation cycle where a component of NO_x is transformed to O₃ in the presence of ultra-violet light. Because of this relationship, if NO_x concentrations decrease, O₃ concentrations increase which is what is observed in Figure 7. O₃ concentrations have remained well below the legal limit for this pollutant across Switzerland for the analysis period.

Looking at the differences after March 16

For the period after March 16 2020, the differences between the observed and predicted values can be aggregated and transformed into percentage change. Table 2 shows these calculations and a plot of the observed and predicted means are displayed in Figure 8. Table 2 demonstrates that NO₂ and NO_x concentrations have decreased by up to 46 and 57 % respectively (at Lugano-Università) based on the scenario modelling technique. Care is needed with interpreting these results however because the uncertainty calculations have not been conducted yet. Additionally, Figure 7 demonstrates that some models showed immediate departure from the observed time series when used to predict at the start of March 2020. This may led to an overestimation of the changes seen in Table 2. Beromünster is the only site with estimated increases in NO₂ and NO_x. The differences between the absolute values are however small: 1.3 and 1.6 µg m^-3 for NO₂ and NO_x respectively (Table 2; Figure 8). These differences are likely to be in the range of uncertainty of the modelling estimates, but further investigation is required to fully to explain this behaviour.

Inversely, O₃ concentrations have increased in almost all locations across Switzerland in response to lower NO_x which is to be expected due to the chemical relationship between NO_x and O₃ within the troposphere (lower atmosphere). A figure visualising the percentage changes is available in Figure 9. Please note that the percentage change calculations are relative to one another and need to be interpreted alongside the concentrations (these data are in Table 2 and Figure 8).

Table 2: Observed and predicted concentration means, deltas, and percentage change for the period after March 16 2020 for Switzerland’s selected monitoring sites.

Site name	Variable	Observed	Predicted	Observed-predicted delta	Predicted-observed percentage change
Lugano-Università	NO2	16.7	31.3	-14.5	-46.4
Bern-Bollwerk	NO2	24.9	38.8	-13.9	-35.7
Lausanne-César-Roux	NO2	22.5	34.2	-11.7	-34.1
Härkingen-A1	NO2	20.9	29.0	-8.0	-27.7
Magadino-Cadenazzo	NO2	13.5	18.6	-5.1	-27.4
Zürich-Kaserne	NO2	23.4	28.4	-5.0	-17.7
Basel-Binningen	NO2	16.4	19.1	-2.7	-14.2
Dübendorf-Empa	NO2	23.7	25.9	-2.2	-8.5
Payerne	NO2	11.1	11.9	-0.8	-7.1
Beromünster	NO2	8.7	7.4	1.3	17.2
Lugano-Università	NOx	18.6	43.4	-24.8	-57.1
Bern-Bollwerk	NOx	41.2	75.1	-33.9	-45.2
Lausanne-César-Roux	NOx	32.1	57.4	-25.3	-44.0
Magadino-Cadenazzo	NOx	16.3	26.4	-10.1	-38.3
Härkingen-A1	NOx	39.1	56.0	-17.0	-30.3
Zürich-Kaserne	NOx	28.8	38.8	-10.0	-25.7
Dübendorf-Empa	NOx	32.9	43.0	-10.1	-23.6
Basel-Binningen	NOx	19.9	24.1	-4.2	-17.3
Payerne	NOx	12.7	15.1	-2.4	-16.1
Beromünster	NOx	9.5	8.0	1.6	19.6
Dübendorf-Empa	O3	50.8	55.9	-5.1	-9.1
Zürich-Kaserne	O3	54.6	57.0	-2.4	-4.2
Payerne	O3	61.5	63.4	-1.9	-3.0
Basel-Binningen	O3	59.8	59.7	0.1	0.2
Härkingen-A1	O3	52.8	52.4	0.4	0.8
Beromünster	O3	76.8	75.4	1.4	1.9
Lausanne-César-Roux	O3	57.5	53.7	3.9	7.2
Magadino-Cadenazzo	O3	57.8	51.3	6.5	12.7
Bern-Bollwerk	O3	50.2	44.1	6.1	13.8
Lugano-Università	O3	61.8	49.8	12.0	24.1

Figure 8: Observed and predicted concentration means for the period after March 16 2020 for Switzerland’s selected monitoring sites.

Figure 9: Predicted and observed percentage change for the period after March 16 2020 for Switzerland’s selected monitoring sites. Sites are ordered by percentage change. Note that the percentage change is a relative change and care is needed in the interpretation.

Related resources

Bastani, A. (2020). Auf lange Sicht: Eine Verschnauf pause für die Natur. Republik. 13.04.2020.

Zogg, C. (2020). Empa researchers monitor air quality—Air pollution in times of corona. Luftanalyse Covid 19.

The Local (2020). How the coronavirus pandemic has improved air quality in Switzerland. The Local. 9 April 2020 14:17 CEST+02:00.

Swiss National News Agency (2020). Reinere luft—aber nicht auf dem land. Bauernzeitung. Publiziert: 09.04.2020 / 16:39 | Aktualisiert: 09.04.2020 / 16:40.

Misicka, S. (2020). Covid-19 and the climate: Can coronavirus help the environment? SWI (swisinfo.ch). April 6, 2020 - 15:09.

Frey, A. (2020). Folge des Lockdown: Es hat wenigerLuftschadstoffe in den Schweizer Städten. Neue Zürcher Zeitung. 04.04.2020, 21.30 Uhr.